An Ensemble of U-Net Models for Kidney Tumor Segmentation With CT Images

Jason Causey; Jonathan Stubblefield; Jake QuallsJennifer FowlerLingrui CaiKarl WalkerYuanfang GuanXiuzhen Huang

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An Ensemble of U-Net Models for Kidney Tumor Segmentation With CT Images

机译：An Ensemble of U-Net Models for Kidney Tumor Segmentation With CT Images

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We present here the Arkansas AI-Campus solution method for the 2019 Kidney Tumor Segmentation Challenge (KiTS19). Our Arkansas AI-Campus team participated the KiTS19 Challenge for four months, from March to July of 2019. This paper provides a summary of our methods, training, testing and validation results for this grand challenge in biomedical imaging analysis. Our deep learning model is an ensemble of U-Net models developed after testing many model variations. Our model has consistent performance on the local test dataset and the final competition independent test dataset. The model achieved local test Dice scores of 0.949 for kidney and tumor segmentation, and 0.601 for tumor segmentation, and the final competition test earned Dice scores 0.9470 and 0.6099 respectively. The Arkansas AI-Campus team solution with a composite DICE score of 0.7784 has achieved a final ranking of top fifty worldwide, and top five among the United States teams in the KiTS19 Competition.

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《IEEE/ACM transactions on computational biology and bioinformatics》 |2022年第3期|1387-1392|共6页
作者
Jason Causey; Jonathan Stubblefield; Jake QuallsJennifer FowlerLingrui CaiKarl WalkerYuanfang GuanXiuzhen Huang;
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作者单位

Department of Computer Science and Molecular Bioscicneces Program, Center for No-Boundary Thinking (CNBT), Arkansas State University, Jonesboro, AR, USA;

Ann Arbor Algorithm, Ann Arbor, MI, USA;

Department of Mathematics and Computer Science, University of Arkansas at Pine Bluff, Pine Bluff, AR, USADepartment of Computational Medicine & Bioinformatics, University of Michigan, Ann Arbor, MI, USA;

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原文格式 PDF
正文语种英语
中图分类生物信息论;
关键词
Tumors; Kidney; Training; Computed tomography; Image segmentation; Predictive models; Biological system modeling;

An Ensemble of U-Net Models for Kidney Tumor Segmentation With CT Images

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